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Thread: The genetic makings of South Asia – IVC as Proto-Dravidian

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    The genetic makings of South Asia – IVC as Proto-Dravidian

    Review (behind paywall) The genetic makings of South Asia, by Metspalu, Monda, and Chaubey, Current Opinion in Genetics & Development (2018) 53:128-133.

    Interesting excerpts (emphasis mine):

    (…) the spread of agriculture in Europe was a result of the demic diffusion of early Anatolian farmers, it was discovered that the spread of agriculture to South Asia was mediated by a genetically completely different farmer population in the Zagros mountains in contemporary Iran (IF). The ANI-ASI cline itself was interpreted as a mixture of three components genetically related to Iranian agriculturalists, Onge and Early and Middle Bronze Age Steppe populations (Steppe_EMBA).

    The first ever autosomal aDNA from South Asia comes from Northern Pakistan (Swat Valley, early Iron Age). This study presented altogether 362 aDNA samples from the broad South and Central Asia and contributes substantially to our understanding of the evolutionary past of South and Central Asia. The study redefines the three genetic strata that form the basis of the Indian Cline. The Indus Periphery (IP) component is composed of (varying proportions of): first, IF, second, Ancient Ancestral South Asians (AASI), which represents an ancient branch of human genetic variation in Asia arising from a population split contemporaneous with the splits of East Asian, Onge and Australian Aboriginal ancestors and third, West_Siberian Hunter gatherers (WS_HG).

    The authors argue that IP could have formed the genetic base of the Indus Valley Civilization (IVC). Upon the collapse of the IVC IP contributes to the formation of both ASI and ANI. ASI is formed as IP admixes further with AASI. ANI in turn forms when IP admixes with the incoming Middle and Late Bronze Age Steppe (Steppe_MLBA) component, (rather than the Steppe_EMBA groups suggested earlier)




    A sketch of the peopling history of South Asia. Depicting the full complexity of available reconstructions is not attempted. Placing of population labels does not indicate precise geographic location or range of the population in question. Rather we aim to highlight the essentials of the recent advancements in the field. We divide the scenario into three time horizons: Panels (a) before 10 000 BCE (pre agriculture era.); (b) 10 000 BCE to 3000 BCE (agriculture era) and (c) 3000 BCE to prehistoric era/modern era. (iron age).

    Dating of the arrival of the Austro-Asiatic speakers in South Asia-based on Y chromosome haplogroup O2a1-M95 expansion estimates yielded dates between 3000 and 2000 BCE [30]. However, admixture LD decay-based approach on genome-wide data suggests the admixture between South Asian and incoming Austro-Asiatic speakers occurred slightly later between 1800 and 0 BCE (Tδtte et al. submitted). It is interesting that while the mtDNA variants of the Mundas are completely South Asian, the Y chromosome variation is dominated at >60% by haplogroup O2a which is phylogeographically nested in East Asian-specific paternal lineages.

    In India, the speakers of Tibeto-Burman (T languages live in the Seven Sisters States in Northeast India and in the very north of the country. Genetically they show a clear East Asian origin and around 20% of subsequent admixture with South Asians within the last 1000 years.The genetic flavour of East Asia in TB is different from that in Munda speakers as the best surrogates for the East Asian admixing component are contemporary Han Chinese.

    I found the simplistic migration maps especially interesting to illustrate ancient population movements. The emergence of EHG is supposed to involve a WHG:ANE cline, though, and this isn’t clear from the map. Also, there is new information on what may be at the origin of WHG and Anatolian hunter-gatherers.

    From the recent Reich’s session on South Asia at ISBA 8:


    Tale of three clines, with clear indication that “Indus Periphery” samples drawn from an already-cosmopolitan and heterogeneous world of variable ASI & Iranian ancestry. (I know how some people like to pore over these pictures – so note red dots = just dummy data for illustration.)
    – Some more certainty about primary window of steppe ancestry injection into S. Asia: 2000-1500 BC

    https://indo-european.eu/2018/10/the...oto-dravidian/
    Kashmir

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    This reviews seem to suggest that there may be some credence to Tony Joseph's idea that the Indus Valley Civilisation may have been related to the Elamites of Bronze Age Iran.
    Last edited by deuterium_1; 01-04-2019 at 01:45 PM.

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    I found the first graphic interesting in that it shows an out of Africa migration at 120,000 BCE (xOoA) with possible contribution (?) to South Asia and >2% contribution to Papuans.

    Do I understand this graphic correctly?

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    Quote Originally Posted by Garvan View Post
    I found the first graphic interesting in that it shows an out of Africa migration at 120,000 BCE (xOoA) with possible contribution (?) to South Asia and >2% contribution to Papuans.

    Do I understand this graphic correctly?
    Yes, that was a conclusion of Metspalu's lab in 2016: Genomic analyses inform on migration events during the peopling of Eurasia. Reich's lab at the same time didn't find it, but said they could have missed the signal if the admixture was very small. (It does seem plausible that some earlier AMHs in Asia would have been assimilated by the main wave in the same way as Neanderthals and Denisovans were.)

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    Re the graphic, fairly certain the 6 red dots are not "dummy" dots but actual samples mind; Reich talks about having 14 Indus_Periphery samples in the preceding slide (if the presentation is the same as https://www.youtube.com/watch?v=Ef4OlJwzxxE dated 17/10/2018) and the context suggests that these 6 are some of those.

    Slide: 8br6QoY.jpg

    14 samples should give them a lot more power to estimate, that the I think, 3 in the pre-print.

    The labeling of the main Swat cluster vs the outliers in OP is also reversed relative to the 17/10/2018 presentation linked above. I'm guessing someone messed up with the slide in OP, since ISBA 8 is earlier on 21/09/2018, and since it fits better with how the blogosphere and even the paper characterised the main Swat samples. Must've been an awkward moment for Reich!

    See - Yd6myT6.png

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    Quote Originally Posted by Eterne View Post
    Re the graphic, fairly certain the 6 red dots are not "dummy" dots but actual samples mind; Reich talks about having 14 Indus_Periphery samples in the preceding slide (if the presentation is the same as https://www.youtube.com/watch?v=Ef4OlJwzxxE dated 17/10/2018) and the context suggests that these 6 are some of those.

    Slide: 8br6QoY.jpg

    14 samples should give them a lot more power to estimate, that the I think, 3 in the pre-print.

    The labeling of the main Swat cluster vs the outliers in OP is also reversed relative to the 17/10/2018 presentation linked above. I'm guessing someone messed up with the slide in OP, since ISBA 8 is earlier on 21/09/2018, and since it fits better with how the blogosphere and even the paper characterised the main Swat samples. Must've been an awkward moment for Reich!

    See - Yd6myT6.png
    That had me confused too as thought the outliers had much higher AASI!
    In fact I did not see a steppe MLBA outlier in poi's analysis - the %ages seem quite linearly distributed from 13.33 to 23.33.

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    Quote Originally Posted by parasar View Post
    That had me confused too as thought the outliers had much higher AASI!
    In fact I did not see a steppe MLBA outlier in poi's analysis - the %ages seem quite linearly distributed from 13.33 to 23.33.
    Agreed. The Steppe MLBA in Swat Valley IA samples seem to be from 0% all the way to 23%, average being ~12% and median being ~14%. So, no sample(in G25) from SwatIA jumps out for their extra high Steppe MLBA. In fact, the highest steppe Swat IndoAryans had lower steppe than the modern NW pops.



    Regarding the highest steppe (~23%) sample in G25, I6893, she clearly has discernible non-steppe (South/West) European admixture. If we model her without any non-steppe European(or LevantN), her extra EEF goes to Sintashta and the fit worsens. Add a non-steppe European group like Iberia_Beaker_No_Steppe and her fit becomes tighter and her Sintashta drops considerably. No other SwatIA sample has that... everybody else is scoring 0% non-steppe Euro, except for her.
    Last edited by poi; 01-04-2019 at 09:58 PM.
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    Quote Originally Posted by parasar View Post
    In fact I did not see a steppe MLBA outlier in poi's analysis - the %ages seem quite linearly distributed from 13.33 to 23.33.
    Yes, the original paper had 56 samples as I count from the supplement, so it's a good probability these outliers have come from doubling the sample size and were not present in that release.

    It seems like the median/mean for the main cluster from Reich's graphic is slightly different from Poi's, but not hugely; might come from different methodology or how new samples have shifted mean.

    I'd note the two outliers are not *that* extreme though (not that I think you have the impression they are, but for the thread) - ANI is fits as about 70:30 Swat Outlier:Swat main, if the plot is accurate, and the prior work from Narasimhan's paper stated that Kalash basically 100% ANI. If the latter holds and solve for those two for the stats we have and the outcome is basically like Tajiks with higher AASI and less (probably none) of the low level East Asian related ancestry Tajiks have (but more ENA ancestry overall, than Tajiks, about same as Kalash).

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    Quote Originally Posted by Eterne View Post
    Yes, the original paper had 56 samples as I count from the supplement, so it's a good probability these outliers have come from doubling the sample size and were not present in that release.

    It seems like the median/mean for the main cluster from Reich's graphic is slightly different from Poi's, but not hugely; might come from different methodology or how new samples have shifted mean.

    I'd note the two outliers are not *that* extreme though (not that I think you have the impression they are, but for the thread) - ANI is fits as about 70:30 Swat Outlier:Swat main, if the plot is accurate, and the prior work from Narasimhan's paper stated that Kalash basically 100% ANI. If the latter holds and solve for those two for the stats we have and the outcome is basically like Tajiks with higher AASI and less (probably none) of the low level East Asian related ancestry Tajiks have (but more ENA ancestry overall, than Tajiks, about same as Kalash).
    Though the picture that noman posted (courtesy Alexander Kim* https://twitter.com/amwkim/status/1042331348504125441) also has 110 (108 + 2 outliers) samples.

    *"David with the final talk of this session, on our lab's South/Central Asian work – now with 550 ancient samples (362 in the @vagheesh Narasimhan et al. preprint version"

    Both can't be right, so perhaps as you suggested the later one was corrected.

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    Quote Originally Posted by Eterne View Post
    Yes, the original paper had 56 samples as I count from the supplement, so it's a good probability these outliers have come from doubling the sample size and were not present in that release.

    It seems like the median/mean for the main cluster from Reich's graphic is slightly different from Poi's, but not hugely; might come from different methodology or how new samples have shifted mean.

    I'd note the two outliers are not *that* extreme though (not that I think you have the impression they are, but for the thread) - ANI is fits as about 70:30 Swat Outlier:Swat main, if the plot is accurate, and the prior work from Narasimhan's paper stated that Kalash basically 100% ANI. If the latter holds and solve for those two for the stats we have and the outcome is basically like Tajiks with higher AASI and less (probably none) of the low level East Asian related ancestry Tajiks have (but more ENA ancestry overall, than Tajiks, about same as Kalash).
    So thats what the Indo Aryans resembled upon entry into SA? Fascinating

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